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The usual process for data sonification is directing digital media of a dataset through a software synthesizer and into a digital-to-analog converter to produce sound for humans to experience. [1] [2] [3] Benefits to interpreting data through sonification include accessibility, pattern recognition, education, and artistic expression. [4]
MIDI music has also been used for similar reasons, but some data is lost in the conversion to MIDI from any other format, unless the music was written with the MIDI standards in mind, which is rare. Digital audio formats such as WAV, mp3, and ogg are used when the audio itself is part of the analysis. Lossy formats such as mp3 and ogg work well ...
Video of air pollution data from Beijing being conveyed as a piece of music. Sonification is the use of non-speech audio to convey information or perceptualize data. [1] Auditory perception has advantages in temporal, spatial, amplitude, and frequency resolution that open possibilities as an alternative or complement to visualization techniques.
For example, one 640 MB compact disc (CD) holds approximately one hour of uncompressed high fidelity music, less than 2 hours of music compressed losslessly, or 7 hours of music compressed in the MP3 format at a medium bit rate. A digital sound recorder can typically store around 200 hours of clearly intelligible speech in 640 MB. [48]
Audio mining is typically split into four components: audio indexing, speech processing and recognition systems, feature extraction and audio classification. [4] The audio will typically be processed by a speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content.
A digital sound recorder from Sony. The most recent and revolutionary developments have been in digital recording, with the development of various uncompressed and compressed digital audio file formats, processors capable and fast enough to convert the digital data to sound in real time, and inexpensive mass storage. [41]
A lossless audio coding format reduces the total data needed to represent a sound but can be de-coded to its original, uncompressed form. A lossy audio coding format additionally reduces the bit resolution of the sound on top of compression, which results in far less data at the cost of irretrievably lost information.
An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...